Abstract
Aiming to solve the problem that ideological and political education courses in universities are not targeted enough and cannot form personalized recommendations, this paper proposes an ideological and political education recommendation system based on analytic hierarchy process (AHP) and improved collaborative filtering algorithm. Firstly, considering the time effect of student scoring, the recommendation model is transformed into Markov decision process. Then, by combining the collaborative filtering algorithm with reinforcing learning rewards and punishments, an optimization model of student scoring based on timestamp information is constructed. To quantify the degree of students' preference for courses, the analytic hierarchy process is used to convert the students' behavior data into the preference value of courses. To solve the problem of data scarcity, the missing values are predicted by the prediction score rounding filling and the optimization boundary completion method. Experimental results show that the feasibility of the proposed system is verified, and the system has vital accuracy and convergence performance. The ideological and political education recommendation system proposed in this paper has important reference significance for promoting ideological and political education in the era of big data.
Highlights
As the main channel to carry out ideological and political work in colleges and universities, ideological and political education curriculum is a critical way to practice the mechanism of education in colleges and universities
To solve the problem that ideological and political education courses in universities are not targeted enough and cannot form personalized recommendations, this paper proposes an ideological and political education recommendation system. e contribution of our paper is summarized, which is grouped into the following three points
Ideological and political education curriculum runs through the whole process of higher education teaching and is an essential system for universities to cultivate high-quality talents
Summary
As the main channel to carry out ideological and political work in colleges and universities, ideological and political education curriculum is a critical way to practice the mechanism of education in colleges and universities. Personalized recommendation can effectively filter unwanted information by analysing users’ behavioural preferences through various recommendation algorithms [2]. Collaborative filtering recommendation algorithm makes recommendations based on users’ intentions [8, 9] It has achieved significant improvement in recommendation accuracy. The existing recommendation algorithm based on deep learning only considers the rating data by using matrix decomposition, which inhibits the recommendation effect [15]. Considering the time effect of student rating, the recommendation model is transformed into Markov decision process. (a) e recommendation model is transformed into Markov decision process considering the time effect of student scoring. (b) An optimization model of student scoring based on timestamp information is constructed by combining the collaborative filtering algorithm with the process of reinforcing learning rewards and punishments.
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